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Abstract

The simulation of imaging systems using Monte Carlo x-ray transport codes is a computationally intensive
task. Typically, many days of computation are required to simulate a radiographic projection image and, as
a consequence, the simulation of the hundreds of projections needed to perform a tomographic reconstruction
may require an unaffordable amount of computing time. To speed up x-ray transport simulations, a MC code
that can be executed in a graphics processing unit (GPU) was developed using the CUDATM programming
model, an extension to the C language for the execution of general-purpose computations on NVIDIA's GPUs.
The code implements the accurate photon interaction models from PENELOPE and takes full advantage of the
GPU massively parallel architecture by simulating hundreds of particle tracks simultaneously. In this work we
describe a new version of this code adapted to the simulation of computed tomography (CT) scans, and allowing the execution in parallel in multiple GPUs. An example simulation of a cardiac CT using a detailed voxelized anthropomorphic phantom is presented. A comparison of the simulation computational performance in one or multiple GPUs and in a CPU (Central Processing Unit), and a benchmark with a standard PENELOPE code, are provided. This study shows that low-cost GPU clusters are a good alternative to CPU clusters for Monte Carlo simulation of x-ray transport.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews